1. Field of the Invention
This invention relates to a method and apparatus for adjusting read-out conditions and/or image processing conditions for a radiation image, wherein read-out conditions, under which a radiation image is to be read out, and/or image processing conditions, under which an image signal representing the radiation image is to be processed, are adjusted on the basis of the image signal representing the radiation image. This invention also relates to a radiation image read-out apparatus for reading out a radiation image from a recording medium, such as a stimulable phosphor sheet, on which the radiation image of an object has been stored, and an image signal representing the radiation image is thereby obtained. This invention further relates to a radiation image analyzing apparatus, wherein characteristic measures representing characteristics of a radiation image, such as read-out conditions under which the radiation image is to be read out, image processing conditions under which the image signal representing the radiation image is to be processed, and the portion of an object the image of which was recorded, are found from an image signal representing the radiation image. This invention still further relates to a radiation image analyzing method, wherein a subdivision pattern of radiation images, the shape and location of an irradiation field, an orientation in which the object was placed when the image of the object was recorded, and/or a portion of an object the image of which was recorded is determined from an image signal representing a radiation image, and a radiation image analyzing apparatus for generating characteristic measures representing the results of the determination.
2. Description of the Prior Art
Techniques for reading out a recorded radiation image in order to obtain an image signal, carrying out appropriate image processing on the image signal, and then reproducing a visible image by use of the processed image signal have heretofore been known in various fields. For example, as disclosed in Japanese Patent Publication No. 61(1986)-5193, an X-ray image is recorded on an X-ray film having a small gamma value chosen according to the type of image processing to be carried out, the X-ray image is read out from the X-ray film and converted into an electric signal (image signal), and the image signal is processed and then used for reproducing the X-ray image as a visible image on a copy photograph, or the like. In this manner, a visible image having good image quality with high contrast, high sharpness, high graininess, or the like can be reproduced.
Also, when certain kinds of phosphors are exposed to radiation such as X-rays, .alpha.-rays, .beta.-rays, .gamma.-rays, cathode rays or ultraviolet rays, they store part of the energy of the radiation. Then, when the phosphor which has been exposed to the radiation is exposed to stimulating rays such as visible light, light is emitted by the phosphor in proportion to the amount of energy stored thereon during its exposure to the radiation. A phosphor exhibiting such properties is referred to as a stimulable phosphor.
As disclosed in U.S. Pat. Nos. 4,258,264, 4,276,473, 4,315,318, 4,387,428, and Japanese Unexamined Patent Publication No. 56(1981)-11395, it has been proposed to use stimulable phosphors in radiation image recording and reproducing systems. Specifically, a sheet provided with a layer of the stimulable phosphor (hereinafter referred to as a stimulable phosphor sheet) is first exposed to radiation which has passed through an object, such as the human body. A radiation image of the object is thereby stored on the stimulable phosphor sheet. The stimulable phosphor sheet is then scanned with stimulating rays, such as a laser beam, which cause it to emit light in proportion to the amount of energy stored thereon during its exposure to the radiation. The light emitted by the stimulable phosphor sheet, upon stimulation thereof, is photoelectrically detected and converted into an electric image signal. The image signal is then used during the reproduction of the radiation image of the object as a visible image on a recording material such as photographic film, on a display device such as a cathode ray tube (CRT) display device, or the like.
Radiation image recording and reproducing systems which use stimulable phosphor sheets are advantageous over conventional radiography using silver halide photographic materials, in that images can be recorded even when the energy intensity of the radiation to which the stimulable phosphor sheet is exposed varies over a wide range. More specifically, since the amount of light which the stimulable phosphor sheet emits when being stimulated varies over a wide range and is proportional to the amount of energy stored thereon during its exposure to the radiation, it is possible to obtain an image having a desirable density regardless of the energy intensity of the radiation to which the stimulable phosphor sheet was exposed. In order to obtain the desired image density, an appropriate read-out gain is set when the emitted light is being detected and converted into an electric signal to be used in the reproduction of a visible image on a recording material, such as photographic film, or on a display device, such as a CRT display device.
In order for an image signal to be detected accurately, certain factors which affect the image signal must be set in accordance with the dose of radiation delivered to the stimulable phosphor sheet and the like. Novel radiation image recording and reproducing systems which accurately detect an image signal have been proposed. The proposed radiation image recording and reproducing systems are constituted such that a preliminary read-out operation (hereinafter simply referred to as the "preliminary readout") is carried out in order approximately to ascertain the radiation image stored on the stimulable phosphor sheet. In the preliminary readout, the stimulable phosphor sheet is scanned with a light beam having a comparatively low energy level, and a preliminary read-out image signal obtained during the preliminary readout is analyzed. Thereafter, a final read-out operation (hereinafter simply referred to as the "final readout") is carried out to obtain the image signal, which is to be used during the reproduction of a visible image. In the final readout, the stimulable phosphor sheet is scanned with a light beam having an energy level higher than the energy level of the light beam used in the preliminary readout, and the radiation image is read out with the factors affecting the image signal adjusted to appropriate values on the basis of the results of an analysis of the preliminary read-out image signal.
The term "read-out conditions" as used hereinafter means a group of various factors, which are adjustable and which affect the relationship between the amount of light emitted by the stimulable phosphor sheet during image readout and the output of a read-out means. For example, the term "read-out conditions" may refer to a read-out gain and a scale factor which define the relationship between the input to the read-out means and the output therefrom, or to the power of the stimulating rays used when the radiation image is read out.
The term "energy level of a light beam" as used herein means the level of energy of the light beam to which the stimulable phosphor sheet is exposed per unit area. In cases where the energy of the light emitted by the stimulable phosphor sheet depends on the wavelength of the irradiated light beam, i.e. the sensitivity of the stimulable phosphor sheet to the irradiated light beam depends upon the wavelength of the irradiated light beam, the term "energy level of a light beam" means the weighted energy level which is calculated by weighting the energy level of the light beam, to which the stimulable phosphor sheet is exposed per unit area, with the sensitivity of the stimulable phosphor sheet to the wavelength. In order to change the energy level of a light beam, light beams of different wavelengths may be used, the intensity of the light beam produced by a laser beam source or the like may be changed, or the intensity of the light beam may be changed by moving an ND filter or the like into and out of the optical path of the light beam. Alternatively, the diameter of the light beam may be changed in order to alter the scanning density, or the speed at which the stimulable phosphor sheet is scanned with the light beam may be changed.
Regardless of whether the preliminary readout is or is not carried out, it has also been proposed to analyze the image signal (including the preliminary read-out image signal) obtained and to adjust the image processing conditions, which are to be used when the image signal is processed, on the basis of the results of an analysis of the image signal. The term "image processing conditions" as used herein means a group of various factors, which are adjustable and set when an image signal is subjected to processing, which affect the gradation, sensitivity, or the like, of a visible image reproduced from the image signal. The proposed method is applicable to cases where an image signal is obtained from a radiation image recorded on a recording medium such as conventional X-ray film, as well as to systems using stimulable phosphor sheets.
As disclosed in, for example, Japanese Unexamined Patent Publication Nos. 60(1985)-185944 and 61(1986)-280163, operations for calculating the values of the read-out conditions for the final readout and/or the image processing conditions are carried out by a group of algorithms which analyze an image signal (or a preliminary read-out image signal). A large number of image signals detected from a large number of radiation images are statistically processed. The algorithms which calculate the read-out conditions for the final readout and/or the image processing conditions are designed on the basis of the results obtained from this processing.
In general, the algorithms which have heretofore been employed are designed such that a probability density function of an image signal is created, and characteristic values are found from the probability density function. The characteristic values include, for example, the maximum value of the image signal, the minimum value of the image signal, or the value of the image signal at which the probability density function is maximum, i.e. the value which occurs most frequently. The read-out conditions for the final readout and/or the image processing conditions are determined on the basis of the characteristic values.
Methods for determining the read-out conditions for the final readout and/or the image processing conditions on the basis of the results of an analysis of the probability density function of an image signal can be classified into the following:
(1) a method as disclosed in Japanese Unexamined Patent Publication No. 60(1985)-156055 wherein both the maximum value and the minimum value in the range resulting in a reproduced visible image containing the necessary image information are determined from a probability density function of an image signal, and, for example, the read-out conditions for the final readout are set such that, during the final readout, the image information represented by values of the emitted light signal falling within the range of the maximum value to the minimum value is detected accurately, PA1 (2) a method as disclosed in Japanese Unexamined Patent Publication No. 60(1985)-185944 wherein only the maximum value is determined from a probability density function of an image signal, a value obtained by subtracting a predetermined value from the maximum value is taken as the minimum value, and the range between the maximum value and the minimum value is taken as the range resulting in a visible image containing the necessary image information, PA1 (3) a method as disclosed in Japanese Unexamined Patent Publication No. 61(1986)-280163 wherein only the minimum value is determined from a probability density function of an image signal, a value obtained by adding a predetermined value to the minimum value is taken as the maximum value, and the range between the minimum value and the maximum value is taken as the range resulting in a visible image containing the necessary image information, PA1 (4) a method as proposed in Japanese Unexamined Patent Publication No. 63(1988)-233658 wherein a difference probability density function is used, PA1 (5) a method as disclosed in Japanese Unexamined Patent Publication No. 61(1986)-170730 wherein a cumulative probability density function is used, and PA1 (6) a method as proposed in Japanese Unexamined Patent Publication No. 63(1988)-262141 wherein a probability density function is divided into a plurality of small regions by using a discrimination standard. The range of an image signal resulting in a visible image containing the necessary image information is determined with one of various methods, and the read-out conditions for the final readout and/or the image processing conditions are set with respect to said range. PA1 i) a storage means for storing information representing a standard pattern of radiation images, PA1 ii) a signal transforming means for transforming said first image signal representing said radiation image into a transformed image signal representing the radiation image, which has been transformed into said standard pattern, and PA1 iii) a condition adjusting means provided with a neural network, which receives said transformed image signal and feeds out information representing the read-out conditions and/or the image processing conditions. PA1 i) a storage means for storing information representing a standard pattern of radiation images, PA1 ii) a signal transforming means for transforming said image signal representing said radiation image into a transformed image signal representing the radiation image, which has been transformed into said standard pattern, and PA1 iii) a condition adjusting means provided with a neural network, which receives said transformed image signal and feeds out information representing the image processing conditions. PA1 i) carrying out a condition adjustment by using a neural network, which receives said first image signal and feeds out information representing the read-out conditions and/or the image processing conditions, and PA1 ii) when learning of said neural network is carried out such that information representing appropriate read-out conditions and/or appropriate image processing conditions may be fed out, utilizing an image signal representing a radiation image, in which a pattern of a specific region of interest in an object is embedded, and read-out conditions and/or image processing conditions, which have been determined as being optimum for the pattern of said region of interest. PA1 i) extracting the image signal components of said first image signal, which represent a pattern of a specific region of interest in said object, said pattern being embedded in said radiation image, by using a neural network, which receives said first image signal made up of a series of image signal components and feeds out information representing the shape and location of the pattern of said region of interest, and PA1 ii) adjusting the read-out conditions and/or the image processing conditions on the basis of the extracted image signal components of said first image signal. PA1 i) carrying out a condition adjustment by using a neural network, which receives said image signal and feeds out information representing the image processing conditions, and PA1 ii) when learning of said neural network is carried out such that information representing appropriate image processing conditions may be fed out, utilizing an image signal representing a radiation image, in which a pattern of a specific region of interest in an object is embedded, and image processing conditions, which have been determined as being optimum for the pattern of said region of interest. PA1 i) extracting the image signal components of said image signal, which represent a pattern of a specific region of interest in said object, said pattern being embedded in said radiation image, by using a neural network, which receives said image signal made up of a series of image signal components and feeds out information representing the shape and location of the pattern of said region of interest, and PA1 ii) adjusting the image processing conditions on the basis of the extracted image signal components of said image signal. PA1 i) carrying out a temporary condition adjustment by using a probability density function analyzing means, which receives said first image signal, temporarily adjusts the read-out conditions and/or the image processing conditions on the basis of the results of an analysis of a probability density function of said first image signal, and feeds out information representing the read-out conditions and/or the image processing conditions, which have been adjusted temporarily, PA1 ii) correcting the read-out conditions and/or the image processing conditions, which have been adjusted temporarily by said probability density function analyzing means, by using a neural network, which receives said first image signal and feeds out information representing correction values to be used in correcting the read-out conditions and/or the image processing conditions, which have been adjusted temporarily, and PA1 iii) thereby finally adjusting the read-out conditions and/or the image processing conditions. PA1 i) carrying out a temporary condition adjustment by using a probability density function analyzing means, which receives said image signal, temporarily adjusts the image processing conditions on the basis of the results of an analysis of a probability density function of said image signal, and feeds out information representing the image processing conditions, which have been adjusted temporarily, PA1 ii) correcting the image processing conditions, which have been adjusted temporarily by said probability density function analyzing means, by using a neural network, which receives said image signal and feeds out information representing correction values to be used in correcting the image processing conditions, which have been adjusted temporarily, and PA1 iii) thereby finally adjusting the image processing conditions. PA1 i) carrying out a temporary condition adjustment by using a probability density function analyzing means, which receives said first image signal, temporarily adjusts the read-out conditions and/or the image processing conditions on the basis of the results of an analysis of a probability density function of said first image signal, and feeds out information representing the read-out conditions and/or the image processing conditions, which have been adjusted temporarily, and PA1 ii) finally adjusting the read-out conditions and/or the image processing conditions by using a neural network, which receives said first image signal and said information representing the read-out conditions and/or the image processing conditions having been adjusted temporarily and feeds out information representing the read-out conditions and/or the image processing conditions, which have been adjusted finally. PA1 i) carrying out a temporary condition adjustment by using a probability density function analyzing means, which receives said image signal, temporarily adjusts the image processing conditions on the basis of the results of an analysis of a probability density function of said image signal, and feeds out information representing the image processing conditions, which have been adjusted temporarily, and PA1 ii) finally adjusting the image processing conditions by using a neural network, which receives said image signal and said information representing the image processing conditions having been adjusted temporarily and feeds out information representing the image processing conditions, which have been adjusted finally. PA1 i) a probability density function analyzing means, which receives said first image signal, temporarily adjusts the read-out conditions and/or the image processing conditions on the basis of the results of an analysis of a probability density function of said first image signal, and feeds out information representing the read-out conditions and/or the image processing conditions, which have been adjusted temporarily, PA1 ii) a neural network, which receives said first image signal and feeds out information representing correction values to be used in correcting the read-out conditions and/or the image processing conditions, which have been adjusted temporarily by said probability density function analyzing means, and PA1 iii) an addition means for adding said correction values, which are represented by the information received from said neural network, to the read-out conditions and/or the image processing conditions, which have been adjusted temporarily by said probability density function analyzing means, and feeding out information representing the read-out conditions and/or the image processing conditions, which have thus been adjusted finally. PA1 i) a probability density function analyzing means, which receives said image signal, temporarily adjusts the image processing conditions on the basis of the results of an analysis of a probability density function of said image signal, and feeds out information representing the image processing conditions, which have been adjusted temporarily, PA1 ii) a neural network, which receives said image signal and feeds out information representing correction values to be used in correcting the image processing conditions, which have been adjusted temporarily by said probability density function analyzing means, and PA1 iii) an addition means for adding said correction values, which are represented by the information received from said neural network, to the image processing conditions, which have been adjusted temporarily by said probability density function analyzing means, and feeding out information representing the image processing conditions, which have thus been adjusted finally. PA1 i) a probability density function analyzing means, which receives said first image signal, temporarily adjusts the read-out conditions and/or the image processing conditions on the basis of the results of an analysis of a probability density function of said first image signal, and feeds out information representing the read-out conditions and/or the image processing conditions, which have been adjusted temporarily, and PA1 ii) a neural network, which receives said first image signal and said information representing the read-out conditions and/or the image processing conditions having been adjusted temporarily and feeds out information representing the read-out conditions and/or the image processing conditions, which have been adjusted finally. PA1 i) a probability density function analyzing means, which receives said image signal, temporarily adjusts the image processing conditions on the basis of the results of an analysis of a probability density function of said image signal, and feeds out information representing the image processing conditions, which have been adjusted temporarily, and PA1 ii) a neural network, which receives said image signal and said information representing the image processing conditions having been adjusted temporarily and feeds out information representing the image processing conditions, which have been adjusted finally. PA1 i) a preliminary read-out means for exposing a stimulable phosphor sheet, on which the radiation image has been stored, to stimulating rays, which cause the stimulable phosphor sheet to emit light in proportion to the amount of energy stored thereon during its exposure to radiation, detecting the emitted, and thereby obtaining a preliminary read-out image signal representing said radiation image of said object, PA1 ii) a final read-out means for again exposing said stimulable phosphor sheet to stimulating rays, which cause said stimulable phosphor sheet to emit light in proportion to the amount of energy stored thereon during its exposure to radiation, detecting the emitted, and thereby obtaining a final read-out image signal representing said radiation image of said object, PA1 iii) a latitude operating means for creating a probability density function of said preliminary read-out image signal, and determining a latitude on the basis of the results of an analysis of the probability density function, the latitude constituting one of read-out conditions, under which said final read-out image signal is to be obtained, and/or image processing conditions, under which said final read-out image signal having been obtained is to be image processed, and PA1 iv) a sensitivity operating means provided with a neural network, which receives said preliminary read-out image signal and feeds out information representing sensitivity, the sensitivity constituting one of the read-out conditions and/or the image processing conditions. PA1 i) a read-out means for reading out a radiation image of an object from a recording media, on which the radiation image has been recorded, and thereby obtaining an image signal representing said radiation image of said object, PA1 ii) a latitude operating means for creating a probability density function of said image signal, and determining a latitude on the basis of the results of an analysis of the probability density function, the latitude constituting one of image processing conditions, under which said image signal is to be image processed, and PA1 iii) a sensitivity operating means provided with a neural network, which receives said image signal and feeds out information representing sensitivity, the sensitivity constituting one of the image processing conditions. PA1 i) using a neural network, which receives said first image signal and feeds out information representing the read-out conditions and/or the image processing conditions, and PA1 ii) feeding information, which represents the position of the center point of the pattern of said object in said radiation image, into said neural network, PA1 i) a means for determining the position of the center point of the pattern of said object in said radiation image from said first image signal, and feeding out information representing the position of the center point of the pattern of said object, and PA1 ii) a neural network, which receives said first image signal and the output of said means for determining the position of the center point of the pattern of said object, adjusts the read-out conditions and/or the image processing conditions on the basis of said first image signal by taking the position of the center point of the pattern of said object into consideration, and feeds out information representing the conditions, which have thus been adjusted. PA1 i) using a neural network, which receives said image signal and feeds out information representing the image processing conditions, and PA1 ii) feeding information, which represents the position of the center point of the pattern of said object in said radiation image, into said neural network, PA1 i) a means for determining the position of the center point of the pattern of said object in said radiation image from said image signal, and feeding out information representing the position of the center point of the pattern of said object, and PA1 ii) a neural network, which receives said image signal and the output of said means for determining the position of the center point of the pattern of said object, adjusts the image processing conditions on the basis of said image signal by taking the position of the center point of the pattern of said object into consideration, and feeds out information representing the conditions, which have thus been adjusted. PA1 i) feeding information, which represents a probability density function of said first image signal, into a neural network, and PA1 ii) feeding out information representing the read-out conditions and/or the image processing conditions from said neural network. PA1 i) feeding information, which represents a probability density function of said first image signal, and subsidiary information, which gives specifics about said radiation image stored on said stimulable phosphor sheet, into a neural network, and PA1 ii) feeding out information representing the read-out conditions and/or the image processing conditions from said neural network. PA1 i) taking the value of said first image signal, which value represents the maximum amount of the emitted light in part of a probability density function of said first image signal other than the part corresponding to a background region in said radiation image, as the maximum value, PA1 ii) normalizing said probability density function with its maximum value in its part between said maximum value and the minimum value of said first image signal, a normalized probability density function being thereby created, PA1 iii) feeding information, which represents said normalized probability density function, into a neural network such that a predetermined value, which falls within the range of the maximum value and the minimum value of the image signal in said normalized probability density function, may always be fed into the same input unit of said neural network, PA1 iv) feeding out information representing the read-out conditions and/or the image processing conditions from said neural network, PA1 v) correcting the read-out conditions and/or the image processing conditions, which are represented by said information fed out from said neural network, on the basis of said predetermined value, and PA1 vi) thereby adjusting the final read-out conditions and/or the final image processing conditions. PA1 i) taking the value of said first image signal, which value represents the maximum amount of the emitted light in part of a probability density function of said first image signal other than the part corresponding to a background region in said radiation image, as the maximum value, PA1 ii) normalizing said probability density function with its maximum value in its part between said maximum value and the minimum value of said first image signal, a normalized probability density function being thereby created, PA1 iii) feeding information, which represents said normalized probability density function, and subsidiary information, which gives specifics about said radiation image stored on said stimulable phosphor sheet, into a neural network such that a predetermined value, which falls within the range of the maximum value and the minimum value of the image signal in said normalized probability density function, may always be fed into the same input unit of said neural network, PA1 iv) feeding out information representing the read-out conditions and/or the image processing conditions from said neural network, PA1 v) correcting the read-out conditions and/or the image processing conditions, which are represented by said information fed out from said neural network, on the basis of said predetermined value, and PA1 vi) thereby adjusting the final read-out conditions and/or the final image processing conditions. PA1 i) a probability density function creating means for creating a probability density function of said first image signal and feeding out information, which represents said probability density function, and PA1 ii) a neural network for receiving said information, which represents said probability density function, from said probability density function creating means, adjusting the read-out conditions and/or the image processing conditions on the basis of said probability density function, and feeding out information representing the read-out conditions and/or the image processing conditions, which have thus been adjusted. PA1 i) a probability density function creating means for creating a probability density function of said first image signal and feeding out information, which represents said probability density function, PA1 ii) a subsidiary information feed-out means for feeding out subsidiary information, which gives specifics about said radiation image stored on said stimulable phosphor sheet, and PA1 iii) a neural network for receiving said information, which represents said probability density function, from said probability density function creating means, receiving said subsidiary information from said subsidiary information feed-out means, adjusting the read-out conditions and/or the image processing conditions on the basis of said probability density function and said subsidiary information, and feeding out information representing the read-out conditions and/or the image processing conditions, which have thus been adjusted. PA1 i) an operation means for creating a probability density function of said first image signal, detecting the value of said first image signal, which value represents the maximum amount of the emitted light in part of a probability density function of said first image signal other than the part corresponding to a background region in said radiation image, taking said value of said first image signal, which has thus been detected from said probability density function, as the maximum value, normalizing said probability density function with its maximum value in its part between said maximum value and the minimum value of said first image signal, a normalized probability density function being thereby created, and feeding out information representing said normalized probability density function, PA1 ii) a neural network for receiving said information, which represents said normalized probability density function, from said operation means such that a predetermined value, which falls within the range of the maximum value and the minimum value of the image signal in said normalized probability density function, may always be fed into the same input unit of said neural network, determining the read-out conditions and/or the image processing conditions on the basis of said normalized probability density function, and feeding out information representing the read-out conditions and/or the image processing conditions, which have thus been determined, and PA1 iii) a correction means for correcting the read-out conditions and/or the image processing conditions, which are represented by said information fed out from said neural network, on the basis of said predetermined value. PA1 i) an operation means for creating a probability density function of said first image signal, detecting the value of said first image signal, which value represents the maximum amount of the emitted light in part of a probability density function of said first image signal other than the part corresponding to a background region in said radiation image, taking said value of said first image signal, which has thus been detected from said probability density function, as the maximum value, normalizing said probability density function with its maximum value in its part between said maximum value and the minimum value of said first image signal, a normalized probability density function being thereby created, and feeding out information representing said normalized probability density function, PA1 ii) a subsidiary information feed-out means for feeding out subsidiary information, which gives specifics about said radiation image stored on said stimulable phosphor sheet, PA1 iii) a neural network for receiving said information, which represents said normalized probability density function, from said operation means, and said subsidiary information from said subsidiary information feed-out means such that a predetermined value, which falls within the range of the maximum value and the minimum value of the image signal in said normalized probability density function, may always be fed into the same input unit of said neural network, determining the read-out conditions and/or the image processing conditions on the basis of said normalized probability density function, and feeding out information representing the read-out conditions and/or the image processing conditions, which have thus been determined, and PA1 iv) a correction means for correcting the read-out conditions and/or the image processing conditions, which are represented by said information fed out from said neural network, on the basis of said predetermined value. PA1 i) feeding information, which represents a probability density function of said image signal, into a neural network, and PA1 ii) feeding out information representing the image processing conditions from said neural network. PA1 i) a probability density function creating means for creating a probability density function of said image signal and feeding out information, which represents said probability density function, and PA1 ii) a neural network for receiving said information, which represents said probability density function, from said probability density function creating means, adjusting the image processing conditions on the basis of said probability density function, and feeding out information representing the image processing conditions, which have thus been adjusted. PA1 i) feeding information, which represents a probability density function of said image signal, and subsidiary information, which gives specifics about said radiation image, into a neural network, and PA1 ii) feeding out information representing the image processing conditions from said neural network. PA1 i) a probability density function creating means for creating a probability density function of said image signal and feeding out information, which represents said probability density function, PA1 ii) a subsidiary information feed-out means for feeding out subsidiary information, which gives specifics about said radiation image, and PA1 iii) a neural network for receiving said information, which represents said probability density function, from said probability density function creating means, receiving said subsidiary information from said subsidiary information feed-out means, adjusting the image processing conditions on the basis of said probability density function and said subsidiary information, and feeding out information representing the image processing conditions, which have thus been adjusted. PA1 i) taking the value of said image signal, which value represents the maximum amount of the emitted light in part of a probability density function of said image signal other than the part corresponding to a background region in said radiation image, as the maximum value, PA1 ii) normalizing said probability density function with its maximum value in its part between said maximum value and the minimum value of said image signal, a normalized probability density function being thereby created, PA1 iii) feeding information, which represents said normalized probability density function, into a neural network such that a predetermined value, which falls within the range of the maximum value and the minimum value of the image signal in said normalized probability density function, may always be fed into the same input unit of said neural network, PA1 iv) feeding out information representing the image processing conditions from said neural network, PA1 v) correcting the image processing conditions, which are represented by said information fed out from said neural network, on the basis of said predetermined value, and PA1 vi) thereby adjusting the final image processing conditions. PA1 i) an operation means for creating a probability density function of said image signal, detecting the value of said image signal, which value represents the maximum amount of the emitted light in part of a probability density function of said image signal other than the part corresponding to a background region in said radiation image, taking said value of said image signal, which has thus been detected from said probability density function, as the maximum value, normalizing said probability density function with its maximum value in its part between said maximum value and the minimum value of said image signal, a normalized probability density function being thereby created, and feeding out information representing said normalized probability density function, PA1 ii) a neural network for receiving said information, which represents said normalized probability density function, from said operation means such that a predetermined value, which falls within the range of the maximum value and the minimum value of the image signal in said normalized probability density function, may always be fed into the same input unit of said neural network, determining the image processing conditions on the basis of said normalized probability density function, and feeding out information representing the image processing conditions, which have thus been determined, and PA1 iii) a correction means for correcting the image processing conditions, which are represented by said information fed out from said neural network, on the basis of said predetermined value. PA1 i) taking the value of said image signal, which value represents the maximum amount of the emitted light in part of a probability density function of said image signal other than the part corresponding to a background region in said radiation image, as the maximum value, PA1 ii) normalizing said probability density function with its maximum value in its part between said maximum value and the minimum value of said image signal, a normalized probability density function being thereby created, PA1 iii) feeding information, which represents said normalized probability density function, and subsidiary information, which gives specifics about said radiation image, into a neural network such that a predetermined value, which falls within the range of the maximum value and the minimum value of the image signal in said normalized probability density function, may always be fed into the same input unit of said neural network, PA1 iv) feeding out information representing the image processing conditions from said neural network, PA1 v) correcting the image processing conditions, which are represented by said information fed out from said neural network, on the basis of said predetermined value, and PA1 vi) thereby adjusting the final image processing conditions. PA1 i) an operation means for creating a probability density function of said image signal, detecting the value of said image signal, which value represents the maximum amount of the emitted light in part of a probability density function of said image signal other than the part corresponding to a background region in said radiation image, taking said value of said image signal, which has thus been detected from said probability density function, as the maximum value, normalizing said probability density function with its maximum value in its part between said maximum value and the minimum value of said image signal, a normalized probability density function being thereby created, and feeding out information representing said normalized probability density function, PA1 ii) a subsidiary information feed-out means for feeding out subsidiary information, which gives specifics about said radiation image, PA1 iii) a neural network for receiving said information, which represents said normalized probability density function, from said operation means, and said subsidiary information from said subsidiary information feed-out means such that a predetermined value, which falls within the range of the maximum value and the minimum value of the image signal in said normalized probability density function, may always be fed into the same input unit of said neural network, determining the image processing conditions on the basis of said normalized probability density function, and feeding out information representing the image processing conditions, which have thus been determined, and PA1 iv) a correction means for correcting the image processing conditions, which are represented by said information fed out from said neural network, on the basis of said predetermined value. PA1 i) an irradiation field determining means for determining the shape and location of an irradiation field of radiation in a radiation image on the basis of a plurality of image signal components representing picture elements in said radiation image, which includes the irradiation field at a part, and PA1 ii) a characteristic measure operating means provided with a neural network, which receives all or some of the image signal components representing the picture elements located in the irradiation field having been determined and feeds out information representing characteristic measures, the characteristic measures representing the characteristics of said radiation image. PA1 i) feeding the image signal into a neural network, and PA1 ii) feeding out information, which represents the results of the determination with respect to the radiation image, from said neural network. PA1 i) an image signal feed-out means for feeding out an image signal representing a radiation image of an object, and PA1 ii) a characteristic measure operating means provided with a neural network, which receives said image signal and feeds out information representing characteristic measures, said characteristic measures representing the results of determination of a subdivision pattern of radiation images, the shape and location of an irradiation field, an orientation in which an object was placed when the image of the object was recorded, and/or a portion of an object the image of which was recorded.
Recently, a method for utilizing a neural network, which is quite different from the algorithms described above, have been proposed.
Such a neural network is provided with a learning function by back propagation method. Specifically, when information (an instructor signal), which represents whether an output signal obtained when an input signal is given is or is not correct, is fed into the neural network, the weight of connections between units in the neural network (i.e. the weight of synapse connections) is corrected. By repeating the learning of the neural network, the probability that a correct answer will be obtained in response to a new input signal can be kept high. (Such functions are described in, for example, "Learning representations by back-propagating errors" by D. E. Rumelhart, G. E. Hinton and R. J. Williams, Nature, 323-9,533-356, 1986a; "Back-propagation" by Hideki Aso, Computrol, No. 24, pp. 53-60; and "Neural Computer" by Kazuyuki Aihara, the publishing bureau of Tokyo Denki University).
The neural network is also applicable when the read-out conditions for the final readout and/or the image processing conditions are to be adjusted. By feeding an image signal, or the like, into the neural network, outputs representing the values of the read-out conditions for the final readout and/or the image processing conditions can be obtained from the neural network.
When the neural network is utilized to adjust the read-out conditions for the final readout and/or the image processing conditions, by repeating the learning of the neural network, the read-out conditions for the final readout and/or the image processing conditions appropriate for a specific radiation image can be determined. However, in a single system for processing X-ray images of, for example, the shoulder of a human body, various types of image signals are obtained which represent various radiation images, such as the images of the right shoulder and the left shoulder (reversed images), an enlarged image and a reduced image, an erect image and a side image and an inverted image, and images shifted from each other. In order for a neural network to be constructed which can determine the read-out conditions for the final readout and/or the image processing conditions appropriate for each of various such images, a very large number of units should be incorporated in the neural network. Also, a storage means should be used which has a very large capacity for storing information representing the weight of connections between units in the neural network. Additionally, the learning of the neural network should be repeated very many times.